Century-scale Methylome Stability in a RecentlyDiverged Arabidopsis thaliana LineageJorg Hagmann1., Claude Becker1., Jonas Muller1, Oliver Stegle2,3, Rhonda C. Meyer4, George Wang1,

Korbinian Schneeberger5, Joffrey Fitz1, Thomas Altmann4, Joy Bergelson6, Karsten Borgwardt2,7¤,

Detlef Weigel1*

1 Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tubingen, Germany, 2 Machine Learning and Computational Biology Research

Group, Max Planck Institute for Developmental Biology and Max Planck Institute for Intelligent Systems, Tubingen, Germany, 3 European Molecular Biology Laboratory,

European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom, 4 The Leibniz Institute of Plant Genetics and Crop Plant

Research, Gatersleben, Germany, 5 Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany, 6 Department of

Ecology and Evolution, University of Chicago, Chicago, Illinois, United States of America, 7 Center for Bioinformatics (ZBIT), Eberhard Karls Universitat Tubingen, Tubingen,

Germany

Abstract

There has been much excitement about the possibility that exposure to specific environments can induce an ecologicalmemory in the form of whole-sale, genome-wide epigenetic changes that are maintained over many generations. In themodel plant Arabidopsis thaliana, numerous heritable DNA methylation differences have been identified in greenhouse-grown isogenic lines, but it remains unknown how natural, highly variable environments affect the rate and spectrum ofsuch changes. Here we present detailed methylome analyses in a geographically dispersed A. thaliana population thatconstitutes a collection of near-isogenic lines, diverged for at least a century from a common ancestor. Methylome variationlargely reflected genetic distance, and was in many aspects similar to that of lines raised in uniform conditions. Thus, evenwhen plants are grown in varying and diverse natural sites, genome-wide epigenetic variation accumulates mostly in aclock-like manner, and epigenetic divergence thus parallels the pattern of genome-wide DNA sequence divergence.

Citation: Hagmann J, Becker C, Muller J, Stegle O, Meyer RC, et al. (2015) Century-scale Methylome Stability in a Recently Diverged Arabidopsis thalianaLineage. PLoS Genet 11(1): e1004920. doi:10.1371/journal.pgen.1004920

Editor: Tetsuji Kakutani, National Institute of Genetics, Japan

Received October 3, 2014; Accepted November 24, 2014; Published January 8, 2015

Copyright: � 2015 Hagmann et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. The DNA and RNA sequencing data have beendeposited at the European Nucleotide Archive under accession numbers PRJEB5287 and PRJEB5331. A GBrowse instance for DNA methylation and transcriptomedata is available at http://gbrowse.weigelworld.org/fgb2/gbrowse/ath_methyl_haplotype1/. DNA methylation data, MR coordinates and genetic variantinformation have also been uploaded to the genome browser of the EPIC consortium (http://genomevolution.org/r/939v).

Funding: This work was supported by a Marie Curie FP7 fellowship (OS), grant NIH GM083068 (JB), FP7 Collaborative Project AENEAS (contract KBBE-2009-226477), a Gottfried Wilhelm Leibniz Award of the DFG, and the Max Planck Society (DW). The funders had no role in study design, data collection and analysis,decision to publish, or preparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

* Email: weigel@weigelworld.org

. These authors contributed equally to the work.

¤ Current address: Department of Biosystems Science and Engineering, ETH, Basel, Switzerland

Introduction

Differences in DNA methylation and other epigenetic marks

between individuals can be due to genetic variation, stochastic

events or environmental factors. Epigenetic marks such as DNA

methylation are dynamic; they can be turned over during mitosis

and meiosis or altered by chromatin remodeling or upon gene

silencing caused by RNA-directed DNA methylation (RdDM).

Moreover, changes in DNA sequence or structure caused by, for

instance, transposable element (TE) insertion, can induce secondary

epigenetic effects at the concerned locus [1,2], or, via processes such

as RdDM, even at distant loci [3–5]. The high degree of sequence

variation, including insertions/deletions (indels), copy number

variants (CNVs) and rearrangements among natural accessions in

A. thaliana provides ample opportunities for linked epigenetic

variation, and the genomes of A. thaliana accessions from around

the globe are rife with differentially methylated regions (DMRs) [6–

10], but it remains unclear how many of these cannot be explained

by closely linked genetic mutations and thus are pure epimutations

[11] that occur in the absence of any genetic differences.

The seemingly spontaneous occurrence of heritable DNA

methylation differences has been documented for wild-type A.thaliana isogenic lines grown for several years in a stable greenhouse

environment [12,13]. Truly spontaneous switches in methylation

state are most likely the consequence of incorrect replication or

erroneous establishment of the methylation pattern during DNA

replication [14–16]. A potential amplifier of stochastic noise is the

complex and diverse population of small RNAs that are at the core

of RdDM [17] and that serve as epigenetic memory between

generations. The exact composition of small RNAs at silenced loci

can vary considerably between individuals [13], and stochastic

inter-individual variation has been invoked to explain differences in

remethylation, either after development-dependent or induced

demethylation of the genome [18,19]. Such epigenetic variants can

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contribute to phenotypic variation within species, and epigenetic

variation in otherwise isogenic individuals has been shown to affect

ecologically relevant phenotypes in A. thaliana [20–22].

In addition to these spontaneous epigenetic changes, the

environment can induce demethylation or de novo methylation

in plants, for example after pathogen attack [23]. Recently, it has

been proposed that repeated exposure to specific environmental

conditions can lead to epigenetic differences that can also be

transmitted across generations, constituting a form of ecological

memory [24–27]. The responsiveness of the epigenome to external

stimuli and its putative memory effect have moved it also into the

focus of attention for epidemiological and chronic disease studies

in animals [28,29]. How the rate of trans-generational reversion

among induced epivariants with phenotypic effects compares to

the strength of natural selection, which in turn determines whether

natural selection can affect the population frequency of epivar-

iants, is largely unknown [30–33].

To assess whether a variable and fluctuating environment is

likely to have long-lasting effects in the absence of large-scale

genetic variation, we have analyzed a lineage of recently diverged

A. thaliana accessions collected across North America. Using a

new technique for the identification of differential methylation, we

found that in a population of thirteen accessions originating from

eight different locations and diverged for more than one hundred

generations, only 3% of the genome had undergone a change in

methylation state. Notably, epimutations at the DNA methylation

level did not accumulate at higher rates in the wild as they did in a

benign greenhouse environment. Using genetic mutations as a

timer, we demonstrate that accumulation of methylation differ-

ences was non-linear, corroborating our previous hypothesis that

shifts in methylation states are generally only partially stable, and

that reversions to the initial state are frequent [12,34]. Many

methylation variants that segregated in the natural North

American lineage could also be detected in the greenhouse-grown

population, indicating that similar forces determined spontaneous

methylation variation, independently of environment and genetic

background. Population structure could be inferred from differ-

ences in methylation states, and the pairwise degree of methylation

polymorphism was linked to the degree of genetic distance.

Together, these results suggest that the environment makes only a

small contribution to durable, trans-generationally inherited

epigenetic variation at the whole-genome scale.

Results

Characterization of the near-isogenic HPG1 lineage fromNorth America

Previous studies of isogenic mutation accumulation (MA) lines

raised in uniform greenhouse conditions identified many appar-

ently spontaneously occurring pure epimutations [12,13]. To

determine whether variable and fluctuating environments in the

absence of large-scale genetic variation substantially alter the

genome-wide DNA methylation landscape over the long term, we

analyzed a lineage of recently diverged A. thaliana accessions

collected across North America. Different from the native range of

the species in Eurasia, where nearly isogenic individuals are

generally only found at single sites, about half of all North

American individuals appear to be identical when genotyped at

139 genome-wide markers [35]. We selected 13 individuals of this

lineage, called haplogroup-1 (HPG1), from locations in Michigan,

Illinois and on Long Island, including pairs from four sites

(Fig. 1A, S1 Table). Seeds of the accessions had been originally

collected between 2002 and 2006 during the spring season, from

plants at the end of their life cycle. Because rapid flowering in the

greenhouse was dependent on an extended cold treatment, or

vernalization, we conclude that the parental plants had germinat-

ed in autumn of the previous year and overwintered as rosettes.

Climate data from the nearest respective weather station

confirmed that precipitation and temperature regimes had varied

considerably between sites in the growing season preceding

collection (S1-S2 Fig.).

Whole-genome sequencing of pools of eight to ten siblings from

each accession identified a shared set of 670,979 single nucleotide

polymorphisms (SNPs) and 170,998 structural variants (SVs)

relative to the Col-0 reference genome, which were then used to

build a HPG1 pseudo reference genome (SOM: Genome analysis

of HPG1 individuals; S2-S3 Table; S3 Fig.). Only 1,354 SNPs and

521 SVs segregated in this population (S4 Table, S4-S5 Fig.),

confirming that the 13 strains were indeed closely related.

Segregating SNPs were noticeably more strongly biased towards

GCRAT transitions than shared SNPs, especially in TEs,

although the bias was not as extreme as in the greenhouse-grown

MA lines (Fig. 1B) [36]. A phylogenetic network and STRUC-

TURE analysis based on the segregating polymorphisms reflected

the geographic origin of the accessions (Fig. 1A, C; S6 Fig.). Three

of the pairs of accessions from the same site were closely related,

and were responsible for many alleles with a frequency of 2 in the

sampled population (Fig. 1D). If the spontaneous genetic mutation

rate is similar to that seen in the greenhouse [36], the HPG1

accessions would be 15 to 384 generations separated from each

other. With a generation time of one year, their most recent

common ancestor would have lived about two centuries ago,

which is consistent with A. thaliana having been introduced to

North America during colonization by European settlers [37].

This is also in line with the fact that in several US herbarium

collections, A. thaliana specimens from the mid-19th century can

be found, among these specimens from the Eastern Seaboard and

the Upper Midwest. We conclude that the HPG1 accessions

constitute a near-isogenic population that should be ideal for the

study of heritable epigenetic variants that arise in the absence of

large-scale genetic change under natural growth conditions.

Because we observed only a weak positive correlation between

Author Summary

It continues to be hotly debated to what extentenvironmentally induced epigenetic change is stablyinherited and thereby contributes to short-term adapta-tion. It has been shown before that natural Arabidopsisthaliana lines differ substantially in their methylationprofiles. How much of this is independent of geneticchanges remains, however, unclear, especially given thatthere is very little conservation of methylation betweenspecies, simply because the methylated sequences them-selves, mostly repeats, are not conserved over millions ofyears. On the other hand, there is no doubt that artificiallyinduced epialleles can contribute to phenotypic variation.To investigate whether epigenetic differentiation, at leastin the short term, proceeds very differently from geneticvariation, and whether genome-wide epigenetic finger-prints can be used to uncover local adaptation, we havetaken advantage of a near-clonal North American A.thaliana population that has diverged under naturalconditions for at least a century. We found that bothpatterns and rates of methylome variation were in manyaspects similar to those of lines grown in stable environ-ments, which suggests that environment-induced changesare only minor contributors to durable genome-wideheritable epigenetic variation.

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genetic distance and phenotypic differences in the greenhouse (S7

Fig.), we also infer that life history differences on their own should

have little effect on the epigenetic landscape.

Spectrum and frequency of single-site DNA methylationpolymorphisms

To assess the long-term heritable fraction of DNA methylation

polymorphisms in the HPG1 lineage, we grew plants under

controlled conditions for two generations after collection at the

natural sites, before performing whole methylome bisulfite

sequencing on two pools of 8–10 individuals per accession (S5

Table). We sequenced pools to reduce inter-individual methylation

variation and fluctuations in methylation rate caused by stochastic

coverage or read sampling bias. After mapping reads to the HPG1

pseudo reference genome, we first investigated epigenetic variation

at the single-cytosine level. There were 535,483 unique differen-

tially methylated positions (DMPs), with an average of 147,975

DMPs between any pair of accessions (SD = 23,745); thus, 86%

of methylated cytosines accessible to our analyses were stably

methylated across all HPG1 accessions. The vast majority of

variable sites (97%) were detected in the CG context (CG-DMPs).

As we have discussed previously [12], this can be largely attributed

to the lower average CHG and CHH methylation rates at

individual sites compared to CG methylation, whereby differences

in methylation rates are smaller and statistical tests of differential

methylation fail more often for CHG and CHH sites..

Additionally, stable silencing-associated methylation of repeats

and TEs, elements rich in CHG and CHH sites, may contribute

to this pattern. That only about 2% of all covered cytosines were

differentially methylated in the relatively uniform HPG1

population contrasted with a previous epigenomic study, in

which most cytosines in the genome were found to be

differentially methylated in 140 genetically divergent accessions

[10]. Fewer than 10% of all cytosines in the genome were never

methylated across these 140 accessions, although most methyl-

ation events were confined to single strains (S9 Table of ref.

[10]). To make our data more comparable to this other study

[10], we identified DMPs of each HPG1 accession against the

Col-0 reference genome. On average we found 383,237 DMPs

per accession, affecting a total of 1,046,892 unique sites. We

estimated that we would have detected 3.6 million DMPs, if we

had sequenced 140 accessions from the HPG1 lineage (see

Materials and Methods; S8 Fig.). The considerably larger

number of DMPs in the 140 accessions [10] is likely due both to

different methodology and to the higher degree of genetic

variation between the analyzed accessions. For example,

Schmitz and colleagues [10] did not directly test for differential

methylation at individual sites nor did they apply multiple

testing correction, which might contribute to the high number

of CHH-DMPs reported in that study.

Fig. 1. Identification of North American accessions that belong to a genetically homogeneous population. (A) Sampling locations ofthe 13 haplogroup-1 (HPG1) strains analyzed in this study. Pie charts indicate population structure inferred from segregating SNP data; SNP: singlenucleotide polymorphism. Data were analyzed using STRUCTURE [66], with K = 6. CT = Connecticut, IL = Illinois, IN = Indiana, MI = Michigan, NJ = NewJersey, NY = New York, WI = Wisconsin. (B) Single-nucleotide mutation spectrum. Bars represent the accession average, error bars indicate 95%confidence intervals. (C) Phylogenetic network of HPG1 accessions based on segregating SNPs and structural variants (SVs) with SplitsTree v.4.12.3[68]. Numbers indicate bootstrap confidence values (10,000 iterations). Dashed line delimits close-up in S6 Fig. (D) Allele frequencies of SNPs and SVs.doi:10.1371/journal.pgen.1004920.g001

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Using the geographic outlier LISET-036 as a reference strain,

we found that 61% of CG-DMPs as well as 36% of the small

number of CHG- and CHH-DMPs were present in at least two

independent accessions (S9A Fig.), many of them shared between

accessions from the same site. As is typical for A. thaliana [38],

most methylated positions clustered around the centromere and

localized to TEs and intergenic regions (Fig. 2A; S9B Fig.). In

contrast, differential methylation in the CG context was over-

represented on chromosome arms, localizing predominantly to

coding sequences (Fig. 2A; S9B Fig.), similar to what we had

previously observed in the greenhouse-grown MA lines [12].

We asked whether DMPs had accumulated more quickly in

natural environments than in the greenhouse, using DNA

mutations in the HPG1 and MA populations as a molecular clock

(SOM: Estimating DMP accumulation rates). Our null hypothesis

was that a variable and highly fluctuating natural environment

increases the rate of heritable methylation changes. In contrast to

this expectation, DMPs appear to have accumulated in sub-linear

fashion in both the HPG1 and MA populations [12] (Fig. 2B) –

with similar trends for DMPs in all three contexts – and the

number of DMPs did not increase more rapidly in the HPG1 than

in the MA lines. The steeper initial increase relative to SNP

differences as well as the broader distribution of MA line

differences relative to HPG1 differences were most likely the

result of having compared individual plants in the MA experiment

[12], rather than pools of siblings, as in the HPG1 experiment.

The effect of pooling individuals, as shown by simulation (S10

Fig.), and a potentially higher genetic mutation rate in the wild

than in the greenhouse, for example because of increased stress

[39], could lead to a slight underestimation of the true HPG1

epimutation rate, but it remains unlikely that it greatly exceeds the

one of the MA lines (SOM: Estimating DMP accumulation rates).

Fig. 2. Epigenetic variation in a nearly isogenic population. (A) Genome-wide features: average coverage in 100 kb windows, the remainder in500 kb windows. Outside coordinates in Mb. (B) Number of DMPs in relation to number of SNPs in pairwise comparisons. Data of mutationaccumulation (MA) lines are based on single individuals, haplogroup1 (HPG1) data on pools of 8–10 individuals; each data point represents anindependent comparison of two lines. DMPs in each pairwise contrast were scaled to the number of methylated sites compared. (C) Annotation ofcytosines in MRs and hDMRs. (hD)MR sequences were assigned to only one annotation in the following order: CDS. intron. UTR. transposon.intergenic. (D) Sequence context of methylated positions relative to MRs and DMRs. (E) Fraction of 5mCGs among all CG sites for each gene andtransposable element, with at least 5 CGs. (F) Minor epiallele frequencies of 2,304 hDMRs that could be split into only two groups and for which atleast four strains showed statistically significant differential methylation. Strains not tested statistically significant for a particular hDMR were notconsidered for this plot. (G) DMRs and hDMRs according to sequence contexts in which significant methylation differences were found. ‘C’ denotes(h)DMRs in all three contexts. Abbreviations: 5mC: methylated position, CDS: coding sequence, DMP: differentially methylated position, DMR:differentially methylated region, hDMR: highly differentially methylated region, HPG1: haplogroup-1 lines, MA: mutation accumulation lines, MRs:methylated regions, SNP: single nucleotide polymorphism, TE: transposable element, UTR: untranslated region.doi:10.1371/journal.pgen.1004920.g002

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Differentially methylated regions in the HPG1 lineageBecause it is unclear whether variation at individual

methylated cytosines has any consequences in plants, we next

focused on differentially methylated regions (DMRs) in the

HPG1 population. A limitation of previous plant methylome

studies using short read sequencing has been that these relied

on integration over methylated or single differentially meth-

ylated sites, or on the analysis of fixed sliding windows along

the genome to identify DMRs. What appears intuitively to be

more appropriate is to first identify regions that are methylated

in individual strains (SOM: Differentially methylated regions)

[40], and to test only these for differential methylation. We

therefore adapted a Hidden Markov Model (HMM), which

had been developed for segmentation of animal methylation

data [41], to the more complex DNA methylation patterns in

plants. We identified on average 32,529 methylated regions

(MRs) per strain (median length 122 bp), with the unified set

across all strains covering almost a quarter of the HPG1

reference genome, 22.6 Mb (Fig. 2A, C; S11A Fig.; S6 Table).

MRs overlapping with coding regions were over-represented in

genes responsible for basic cellular processes (p-value ,,

0.001), in agreement with gene body methylation being a

hallmark of constitutively expressed genes [42]. Only 1% ofmCHH and 2% of mCHG positions were outside of methylated

regions (Fig. 2D), consistent with the dense CHH and CHG

methylation found in repeats and silenced TEs [38]. Com-

pared to mCGs within methylated regions, mCGs in unmethy-

lated space localized almost exclusively to genes (94%), were

spaced much farther apart, and were separated by many more

unmethylated loci (Fig. 2E; S11B-C Fig.). This explains why

sparsely methylated genes were under-represented in HMM-

determined methylated regions, even though gene body

methylation accounts for a large fraction of methylated CG

sites. The accuracy of our MR detection method was well

supported by independent methods (SOM: Validation of

methylated regions).

Using the unified set of MRs, we tested all pairs of accessions

for differential methylation, identifying 4,821 DMRs with an

average length of 159 bp (S12 Fig.; S11A Fig.; S7 Table). Of

the total methylated genome space, only 3% were identified as

being differentially methylated, indicating that the heritable

methylation patterns had remained largely stable in this set of

geographically dispersed accessions. Indeed, 91% of genic and

98% of the TE sequence space were devoid of DMRs. Of the

DMRs, 3,199 were classified as highly differentially methylated

(hDMRs; S8 Table), i.e. they had a more than three-fold

change in methylation rate and were longer than 50 bp. The

DMR allele frequency spectrum was similar to that of variably

methylated single sites (Fig. 2F). Most DMRs and hDMRs

showed statistically significant methylation variation in only

one cytosine context, often CG (Fig. 2G), even though DMRs

were dominated by CHG and CHH methylation (Fig. 2D, S13

Fig.). Different from individual sites (DMPs), the densities for

DMRs and hDMRs were highest in centromeric and pericen-

tromeric regions, and overlapped more often with TEs than

with genes (Fig. 2A, C). Relative to all methylated regions,

genic regions were two-fold overrepresented in the genome

sequence covered by DMRs, and three-fold in hDMRs

(Fig. 2C). Currently, we do not know whether this simply

reflects the greater power of detecting differential methylation

at the typically more highly methylated CG sites compared

to CHG or CHH sites, or whether this reflects actual

biology.

Methylation variation and transcriptome changesDNA methylation in gene bodies has been proposed to exclude

H2A.Z deposition and thereby stabilize gene expression levels

[42]. We therefore asked what impact differential methylation had

on transcriptional activity. We identified 269 differentially

expressed genes across all possible pairwise combinations (S9-

S10 Table), most of which were found in more than one

comparison. When we clustered accessions by differentially

expressed genes, closely related pairs were placed together (S14

Fig.). We identified 28 differentially expressed genes that

overlapped with an hDMR either in their coding or 1 kb

upstream region, but the relationship between methylation and

expression was variable (S11 Table). By visual examination, we

found not more than five instances of demethylation that were

associated with increased expression; examples are shown in S15

Fig.

Comparison of epimutations in natural and greenhouse-grown lineages

With the caveat that there are uncertainties about the genetic

mutation rate in the wild, and therefore how the number of SNPs

relates to the number of generations since the last common

ancestor, there was no evidence for faster accumulation of variably

methylated sites in the HPG1 population, nor for very different

epimutation rates among HPG1 lines (Fig. 2B). Importantly, the

overlap of differential methylation between the two populations

was much greater than expected by chance: the probability of a

random mC site in the MA population of being variably

methylated in the HPG1 population was only 7%, but it was

41% among sites that were also variably methylated in the MA

population – a six-fold enrichment (four-fold enrichment in the

reciprocal comparison; Fig. 3A). In other words, almost half of the

DMPs in the MA lines were also polymorphic in the HPG1 lines,

and almost a third of HPG1 DMPs were also variably methylated

in the MA population. These shared DMPs were more heavily

biased towards the chromosome arms and towards genic

sequences than population-specific epimutations (S16A-S16B

Fig.). Conversely, DMPs unique to one population were more

likely to be unmethylated throughout the other population when

compared to random methylated sites (Fig. 3A), as one might

expect for sites that sporadically gain methylation.

DMPs unique to the HPG1 lineage appeared to be less frequent

in the pericentromere compared to MA- line-specific DMPs (S16A

Fig.), which was also reflected in an apparently higher epimutation

frequency in the MA lines for these regions (S16B Fig.). We

therefore investigated whether the annotation spectrum differed

between these two classes of differentially methylated sites. Even

though MA-specific DMPs were more often found in TEs

compared to HPG1-specific DMPs, this bias was also observed

for all cytosines accessible to our methylome analyses (S16C Fig.),

and can therefore be explained by a more accurate read mapping

and better TE annotation in the Col-0 reference compared to the

HPG1 pseudo-reference genome. Indeed, except for chromosome

4, the average sequencing depth in the pericentromere was higher

in the MA lines (S16B Fig.).

DMPs distinguishing MA lines that were separated from each

other by only a few generations were more frequently variably

methylated in the HPG1 lineage than DMPs identified between

distant MA lines (S17 Fig.). We interpret this observation as an

indication of privileged sites that are more labile and therefore

more likely to have already changed in status after a small number

of generations.

We used the methods implemented for the HPG1 population to

detect DMRs also in the MA strains. Similar to variable single

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positions, or DMPs, the overlap between 2,523 DMRs of the MA

lines that we could map to the HPG1 methylated genome space

with the 4,821 DMRs of the HPG1 accessions was greater

than expected and highly significant (F-score = 32.9; 100,000

permutations). HPG1 DMRs were four-fold more likely to

coincide with MA DMRs than with a random methylated region

from this set (Fig. 3B). We observed similar degrees of overlap

independently of sequence context. Shared DMRs between both

lineages were, in contrast to shared DMPs, not biased towards

genic regions (S18 Fig.). Differentially methylated regions in the

HPG1 lineage, however, overlapped with genic sequences more

often than MA DMRs (S18 Fig.), which might again be explained

by the different efficiencies in mapping to repetitive sequences and

TEs (S16B Fig.).

We next wanted to know how this short-term variation

compared to methylation variation across much deeper splits.

To this end, we identified variably methylated regions between a

randomly chosen MA line and a randomly chosen HPG1 line;

these DMRs, which differentiate distantly related accessions, were

also enriched in each of the two sets of within-population DMRs

(MA or HPG1) (Fig. 3C). Finally, we compared DMRs found in

the HPG1 population to DMRs that had been identified with a

different method among 140 natural accessions from the global

range of the species [10] (Fig. 3D). Although only 9,994, less than

one fifth, of the variable regions from the global accessions were

covered by methylated regions in the HPG1 strains, the overlap of

DMRs was highly significant (F-score = 19.8; 100,000 permuta-

tions). Together, the high recurrence of differentially methylated

sites and regions from different datasets points to the same loci

being inherently biased towards undergoing changes in DNA

methylation independently of genetic background and growth

environment.

To explore potential sources of such lability, we compared

variation in the HPG1 lines to that caused by mutations in various

components of epigenetic silencing pathways [43]. Almost all

variable sites and regions in CG-methylated parts of the HPG1

genome were hypomethylated in mutants deficient in DNA

methylation maintenance, most notably in the met1 single and

the vim123 triple mutants (S19 Fig.). This is consistent with

polymorphic methylation arising primarily because of errors in the

maintenance of symmetrical CG methylation during DNA

replication. Hypermethylated sites in the rdd triple mutant, which

shows impaired demethylation, were also found slightly more often

within variably methylated regions of all contexts (S19D Fig.).

Heritability and genetic linkage of epigenetic variationTo quantify how many methylation differences were co-

segregating with genome-wide genetic changes at both linked

and unlinked sites, we estimated heritability for each highly

differentially methylated region by applying a linear mixed model-

based method. We used segregating sequence variants with

complete information as genotypic data and average methylation

rates of hDMRs with complete information as phenotypes. The

median heritability of all hDMRs was 0.41 (mean 0.44), which

means that genetic variance across the entire genome contributed

less than half of methylation variance (Fig. 4A). hDMRs in the

HPG1 strains that were not methylated in the greenhouse-grown

MA lines had a higher median heritability, 0.48, than HPG1

hDMRs also found among MA DMRs (0.29), which held true for

all sequence contexts (Fig. 4A; S20 Fig.). Regions of highly

differential methylation found only in the HPG1 population,

especially those in unmethylated regions of the MA lines, were

thus more likely to be linked to whole-genome sequence variation

than hDMRs found in both populations. For 19% of all hDMRs

Fig. 3. Overlapping epigenetic variation in independent populations. (A) Comparison of methylated positions (5mCs) and differentiallymethylated positions (DMPs) identified in pairwise comparisons of mutation accumulation (MA) [12] and haplogroup-1 (HPG1). Distinction in differentsequence contexts has been omitted since almost all DMPs (.97%) are in CG context. Left: sites in HPG1 strains and their status in the MA data; right:sites in the MA strains and their status in the HPG1 data. N-DMPs: non-differentially methylated positions. (B) Comparison of methylated regions(MRs) and differentially methylated regions (DMRs) identified in pairwise comparisons of HPG1 and MA lines. Dark and light orange subsets of DMRsdistinguish regions with differential methylation occurring exclusively in CG context (CG-DMRs) or in any additional or alternative context(s) (C-DMRs).Left: regions in HPG1 strains and their status in the MA data; right: regions in the MA strains and their status in the HPG1 data. (C) MRs and DMRsidentified in comparison between one randomly chosen MA line (30–39) and one randomly chosen HPG1 line (MuskSP-68), and their overlap withwithin-population DMRs. (D) Comparison of HPG1 DMRs with CG-DMRs (dark orange) from ref [10] and C-DMRs (light orange) identified in 140 naturalA. thaliana accessions. Because methylated regions were not reported in ref. [10], the overlap of DMRs with the space not covered by DMRs could notbe assessed. N-DMRs: non-differentially methylated regions.doi:10.1371/journal.pgen.1004920.g003

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(21% CG-hDMRs, 14% CHG-hDMRs, 7% CHH-hDMRs), the

whole-genome genotype explained more than 90% of their

methylation differences (with a standard error of at most 0.1).

Of these hDMRs, half had a heritability of greater than 0.99. That

6.7% of the sequence space of these heritable hDMRs still

overlapped with MA DMRs (versus 9.4% for the less heritable

hDMRs) was in agreement with the hypothesis that there are

regions that vary highly in their methylation status independently

of genetic background.

To identify genetic variants that potentially directly cause

methylation changes in their local genomic neighborhood, we

focused on variably methylated regions that were within 1 kb of

segregating SNPs or indels. Of 191 such DMRs, only three showed

a systematic correlation with nearby sequence polymorphisms. We

noticed, however, that coding regions with structural variants

larger than 20 bp that distinguished the MA and HPG1

populations were more likely to be methylated in both lineages

than non-polymorphic coding regions (Fig. 4B). Consequently,

DMPs unique to the HPG1 lines were on average closer to

insertions or deletions than DMPs shared between the HPG1 and

MA populations (Fig. 4C).

Lastly, we asked whether the genome-wide methylation pattern

reflected genetic relatedness, i.e., population structure. Hierarchical

clustering by methylation rates of variable sites and regions grouped

strains by sampling location (Fig. 4D, E). This result was largely

independent of the sequence or the annotation context of these loci,

and not seen with sites that our statistical tests had identified as

stably methylated (S21 Fig.). That variably methylated regions

grouped the accessions similar to DMPs, albeit with less confidence

(shorter branch lengths; S21 Fig.), suggested that our DMR calling

algorithm was conservative. Methylation data thus paralleled

similarity between accessions at the genetic level, in agreement

with the interpretation that methylation differences primarily reflect

the number of generations since the last common ancestor.

Fig. 4. Genetic effects on epigenetic variation. (A) Heritability values based on genome-wide genetic differentiation for all highly differentiallymethylated regions (hDMRs), hDMRs with randomly permuted methylation rates and subsets of hDMRs depending on their overlap with methylatedand differentially methylated regions of the mutation accumulation (MA) population, respectively. P: Permuted (2,945 hDMRs); A: All (2,945); U:Unmethylated in MA (1,310); M: Methylated in MA (1,243); D: DMR in MA (392). (B) Correlation between structural variants (SVs) and probability ofoverlap with methylated regions (MRs). Divergent sequences are insertions of at least 20 bp relative to the other population. This analysis is based on3,256 SVs overlapping with genes, 641 with coding sequences (CDS) and 4,020 with transposable elements (TEs) (S12 Table). (C) Distances betweencommon SVs of at least 20 bp and the closest differentially methylated position (DMP), depending on whether it is shared between the mutationaccumulation (MA) and haplogroup-1 (HPG1) populations. Triangles represent the mean. (D) Hierarchical clustering of HPG1 strains based onmethylation rates at 50,000 CG-DMPs. (E) Hierarchical clustering of HPG1 strains based on average methylation rates of 2,829 hDMRs with fullinformation across all strains. Methylation rates per region were calculated as the average methylation rate of each methylated cytosine in thatregion.doi:10.1371/journal.pgen.1004920.g004

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Discussion

We have tested the hypothesis that accumulation of epigenetic

variation under natural conditions proceeds over the short term in

a very different manner than the clock-like behavior of genetic

variation [24–27]. To this end, we have taken advantage of a

unique natural experiment, the A. thaliana HPG1 lineage, which

has likely diverged for at least a century throughout North

America. Our analyses have revealed little evidence for broad-

scale and durable epigenetic differentiation that might have been

induced by the variable and fluctuating environmental conditions

experienced by the HPG1 accessions since they separated from

each other. While the exact conditions these plants have been

subjected to since their separation from a common ancestor

remain unknown, the time scale and diversity of geographic

provenance are strong indicators of the variability of the

environment between the different sampling sites, supported by

temperature and precipitation data from nearby weather moni-

toring stations. The general analytical framework enabled by the

HPG1 lineage – nearly isogenic lines grown for more than a

century under variable and fluctuating conditions – could not have

been achieved in a controlled greenhouse experiment.

Studies of epiRIL populations have shown that pure epialleles

can be stably transmitted across several generations [5,19], but

how often this is the case for environmentally induced epigenetic

changes has been heavily debated [33,44–46]. The recent

excitement about the transmission of induced epigenetic variants

comes from such variants having been proposed to be more often

adaptive than random genetic mutations [24–26]. Contrary to the

expectations discussed above, we found that epimutation rates

under natural growth conditions at different sites did not differ

substantially from those observed in a controlled greenhouse

environment, with polymorphisms accumulating sub-linearly in

both situations, apparently because of frequent reversions. Note

that we grew the HPG1 plants under controlled conditions for two

generations after sampling at the natural site, to reduce the range

of epigenetic variation to the long-term heritable fraction. Given

that the environment can induce acute methylation changes

[23,47], it is likely that we would have observed greater epigenetic

variation, if we had sampled field-grown individuals directly.

However, most of such variation induced during ontogeny does

not appear to be heritable, as we did not find evidence for it after

two extra generations in the greenhouse. Additional studies that

directly compare plants grown outdoors to their progeny grown in

a stable and controlled environment will help to further clarify this

issue.

We found that positions of differential methylation in the HPG1

population are more likely to overlap with DMPs detected

between closely related MA lines than between more distantly

related MA lines. This observation supports the hypothesis that

there are different classes of polymorphic sites. One of these

includes ‘high lability’ sites that are independent of the genetic

background, that change with a high epimutation rate, and that

are therefore more likely to appear in each population. Another

class of DMPs comprises more stable sites that gain or lose

methylation more slowly and that therefore are less likely to be

shared between different populations.

Differences between accessions in terms of DNA methylation

recapitulated their genetic relatedness, further corroborating our

hypothesis that heritable epigenetic variants arise predominantly

as a function of time rather than as a consequence of rapid local

adaptation. Epigenetic divergence thus does not become uncou-

pled from genetic divergence when plants grow in varying

environments, nor does the rate of epimutation noticeably

increase. A minor fraction of heritable epigenetic variants may

be related to habitat, which could be responsible for LISET-036

being epigenetically a slight outlier (Fig. 4E), even though it is not

any more genetically diverged from the most recent common

ancestor of HPG1 than other lines. Such local epigenetic footprints

could also explain fluctuations in epimutation frequency between

the MA and HPG1 lineages. Subtle adaptive changes at a limited

number of loci would go unnoticed in the present analysis of

genome-wide patterns and can therefore not be excluded.

However, on a genome-wide scale there was little indication of

adaptive change: neither were LISET-036 specific regions of

differential methylation in and near genes enriched for GO terms

with an obvious connection to environmental adaptation, nor were

there overlapping differentially expressed genes (S22 Fig., SOM:

Analysis of LISET-036 specific hDMRs). In combination with the

general lack of correlation between differential methylation and

changes in gene expression, our findings suggest that epigenetic

changes in nature are mostly neutral, and thus comparable to

genetic mutations. We point out that an annual species such as A.thaliana might be differently disposed to record environmental

signals in its epigenome compared to more long-lived species.

From an evolutionary perspective, in perennial species the

advantage of epigenetically mediated local adaptation to changing

conditions could be more pronounced, and future studies are

warranted to address this question.

Because of the near-isogenic background of the HPG1

accessions, we were also able to gauge how much of epigenetic

variation is either caused by, or stably co-segregates with genetic

differences. HPG1-specific highly differentially methylated regions

were more often linked to genotype variation than regions that

were variably methylated in both the HPG1 and MA populations.

This suggests that heritable hDMRs can, to a certain extent, be

considered facilitated epigenetic changes [11].

Both differentially methylated regions and positions are over-

represented in genes, but TEs and intergenic regions contain many

variable regions and only very few variable single sites. Altogether

our data indicate that both variably and constitutively methylated

positions in genes are typically separated by many unmethylated

sites and that a large fraction of these is therefore not classified as

being (differentially) methylated. Variability of DNA methylation

in plant genes thus mainly affects single, sparsely distributed

cytosines, the biological relevance of which remains unclear.

Our comparisons between MA laboratory strains and natural

HPG1 accessions have revealed that loci of variable methylation

overlapped much more between the two groups than expected by

chance, despite these populations having experienced very

different environments that also differ greatly in their uniformity,

and despite completely different genetic backgrounds. The

observation that changes at many sites and loci are independent

of the genetic background and geographic provenance suggests

that spontaneous switches in methylation predominantly reflect

intrinsic properties of the DNA methylation and gene silencing

machinery, with the CG maintenance system seemingly being the

most error-prone. Our most important finding is probably that

DNA methylation is highly stable across dozens, if not hundreds of

generations of growth in natural habitats; 97% of the total

methylated genome space was not contained in a DMR. The stark

contrast to published data, which describes more than 90% of

cytosines in the genome as variably methylated in a set of 140

divergent natural accessions [10], can be explained both by the

low amount of genetic divergence among the HPG1 accessions

and by methodological differences. For future studies, we

recommend the application of non-permissive statistical tests in

the analysis of differential methylation. The overall stability of

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methylation presented here is in accordance with the high

similarity of methylation in evolutionarily conserved gene

sequences [48]. It contrasts, however, with our recent report

showing that over longer evolutionary distances that separate

species in the same genus or closely related genera, there is very

little conservation of global DNA methylation, simply because the

sequences that are typically methylated are much more evolu-

tionarily fluid than non-methylated sites [47]. In summary, we

propose that the stability of DNA methylation first and foremost

depends on the stability of the underlying genetic sequence and

that heritable polymorphisms that arise in response to specific

growth conditions appear to be much less frequent than those that

arise spontaneously. These conclusions are of importance when

considering epimutations as a potential evolutionary force.

Materials and Methods

Plant growth and materialAccessions [35] were collected in the field at locations indicated

in S1 Table. Seeds had been bulked in the Bergelson lab at the

University of Chicago before starting the experiment. Plants were

then grown at the Max Planck Institute in Tubingen on soil in

long-day conditions (23uC, 16 h light, 8 h dark) after seeds had

been stratified at 4uC for 6 days in short-day conditions (8 h light,

16 h dark). We grew one plant of each accession under these

conditions; seeds of that parental plant were then used for all

experiments. Eight plants of the same accession were grown per

pot in a randomized setup. All accessions used in this paper have

been added to the 1001 Genomes project (http://1001genomes.

org) and have been submitted to the stock center.

Nucleic acid extractionDNA was extracted from rosette leaves pooled from eight to ten

individual adult plants. Plant material was flash-frozen in liquid

nitrogen and ground in a mortar. The ground tissue was

resuspended in Nuclei Extraction Buffer (10 mM Tris-HCl

pH 9.5, 10 mM EDTA, 100 mM KCl, 0.5 M sucrose, 0.1 mM

spermine, 0.4 mM spermidine, 0.1% b-mercaptoethanol). After

cell lysis in nuclei extraction buffer containing 10% Triton-X-100,

nuclei were pelleted by centrifugation at 2000 g for 120 s.

Genomic DNA was extracted using the Qiagen Plant DNeasy

kit (Qiagen GmbH, Hilden, Germany). Total RNA was extracted

from rosette leaves pooled from eight to ten individual adult plants

using the Qiagen Plant RNeasy Kit (Qiagen GmbH, Hilden,

Germany). Residual DNA was eliminated by DNaseI (Thermo

Fisher Scientific, Waltham, MA, USA) treatment.

Library preparationDNA libraries for genomic and bisulfite sequencing were

generated as described previously [12]. Libraries for RNA

sequencing were prepared from 1 mg of total RNA using the

TruSeq RNA sample prep kit from Illumina (Illumina) according

to the manufacturer’s protocol.

SequencingAll sequencing was performed on an Illumina GAII instrument.

Genomic and bisulfite-converted libraries were sequenced with

26101 bp paired-end reads. For bisulfite sequencing, conventional

A. thaliana DNA genomic libraries were analyzed in control lanes.

Transcriptome libraries were sequenced with 101 bp single end

reads. Four libraries with different indexing adapters were pooled

in one lane; no control lane was used. For image analysis and base

calling, we used the Illumina OLB software version 1.8.

Processing of genomic readsThe SHORE pipeline v0.9.0 [49] was used to trim and quality-

filter the reads. Reads with more than 2 (or 5) bases in the first 12

(or 25) positions with a base quality score of less than 4 were

discarded. Reads were trimmed to the right-most occurrence of

two adjacent bases with quality values equal to or greater than 5.

Trimmed reads shorter than 40 bases and reads with more than

10% (of the read length) of ambiguous bases were discarded.

Reads were aligned against the Arabidopsis thaliana genome

sequence version TAIR9 in iteration 1 and against updated

‘‘Haplogroup 1-like’’ genomes in further iterations. The mapping

tool GenomeMapper v0.4.5s [50] was used, allowing for up to

10% mismatches and 7% single-base-pair gaps along the read

length to achieve high coverage. All alignments with the least

amount of mismatches for each read were reported. A paired-end

correction method was applied to discard repetitive reads by

comparing the distance between reads and their partner to the

average distance between all read pairs. Reads with abnormal

distances (differing by more than two standard deviations) were

removed if there was at least one other alignment of this read in a

concordant distance to its partner. The command line arguments

used for SHORE are listed in S1 File.

Genetic variant identificationGenetic variants were called in an iterative approach. In each

step, SNPs and structural variants common to all strains were

detected and incorporated into a new reference genome. The thus

refined ‘‘HPG1-like’’ genomes served as the reference sequence in

the subsequent iterations (S3 Fig.). We performed three iterations

to call segregating variants and built two reference genomes to

retrieve common polymorphisms. The steps performed in each

iteration are described in the following paragraphs.

SNP and small indel callingBase counts on all positions were retrieved by SHORE v0.9.0

[49] and a score was assigned to each site and variant (SNP or

small indel of up to 7% of read length) depending on different

sequence and alignment-related features. Each feature was

compared to three different empirical thresholds associated with

three different penalties (40%, 20% and 5% reduction of the score,

initial score: 40). They can be found in S13 Table.

For comparisons across lines, positions were accepted if at most

one intermediate penalty on their score was applicable to at least

one strain (score $32). In this case, the threshold for the other

strains was lowered, accepting at most one high and two

intermediate penalties (score $15). In this way, information from

other strains was used to assess sites from the focal strain under the

assumption of mostly conserved variation, allowing the analysis of

additional sites.

Only sites sufficiently covered ($5x) and with accepted base

calls in at least half of all strains ($7 out of 13) were processed

further. Variable alleles with a frequency of 100% were classified

as "common" and variants with a lower frequency as "segregat-

ing".

Additional SNPs were called using the targeted de novoassembly approach described below.

Structural variant (SV) callingAlthough a plethora of SV detection tools have been developed,

the predicted variants show little overlap between each other on

the same data sets. Furthermore, the false positive rate of many

methods can be drastic [51]. Hence, rather than taking the

intersection of the output from different tools, which would yield

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only a small number of SVs, we combined different tools and

applied a stringent evaluation routine to identify as many true SVs

as possible. Since SVs common to all strains should be

incorporated into a new reference, only methods that identify

SVs on a base pair level could be used. Currently, there are four

different SV detection strategies (based on depth of coverage,

paired-end mapping, split read alignments or short read assembly,

respectively). Only tools based on split read alignments and

assemblies are capable of pinpointing SV breakpoints down to the

exact base pair. Programs that were used include Pindel v2.4t [52],

DELLY v0.0.9 [53], SV-M v0.1 [54] and a custom local de novoassembly pipeline targeted towards sequencing gaps (described

below). We reported deletions and insertions from all tools, and

additionally inversions from Pindel. DELLY combines split read

alignments with the identification of discordant paired-end

mappings. Thus, our SV calling made use of three out of four

currently available methodologies.

Reads for DELLY were mapped using BWA v0.6.2 [55] against

the TAIR9 Col-0 reference genome to produce a BAM file as

DELLY’s input format.

The arguments for the command line calls of all tools are listed

in S1 File.

Targeted de novo assemblyWhile using a re-sequencing strategy, there are regions without

read coverage (‘‘sequencing gaps’’) because either the underlying

sequence is being deleted in the newly sequenced strain, or highly

divergent to the reference sequence, or present in the focal strain,

but not represented in the read set. To access sequences in the first

two classes, a local de novo assembly method was developed.

Insertion breakpoints or small deletions, however, can mostly

not be detected by zero coverage due to reads ranging with a few

base pairs into or beyond the structural variants. Therefore, we

defined a ‘‘core read region’’ as the read sequence without the first

and last 10 nucleotides. To be able to assemble the latter cases, the

definition of ‘‘sequencing gaps’’ was extended from zero-covered

regions to stretches not spanned by a single read’s core region.

All reads aligned to the surrounding 100 nucleotides of such

newly defined sequencing gaps as well as the unmappable reads

from the re-sequencing approach together with their potential

mapped partners constituted the assembly read set. Two assembly

tools were used to generate contigs, SOAPdenovo2 v2.04 [56] and

Velvet v1.2.0 [57] (command line arguments in S1 File). Contigs

shorter than 200 bp were discarded. To map the remaining

contigs of each assembler against the iteration-specific reference

genome, their first and last 100 bp were aligned with Genome-

Mapper v0.4.5s [50], accepting a maximal edit distance of 10. If

both contig ends mapped uniquely within 5,000 bp, the thus

framed region on the reference was aligned to the contig using a

global sequence alignment algorithm after Needleman-Wunsch

(‘needle’ from the EMBOSS v6.3.1 package). In addition, non-

mapping contigs were aligned with blastn (from the BLAST

v2.2.23 package) [58] to yield even more variants.

All differences between contig and reference sequences were

parsed (including SNPs, small indels and SVs) for each assembly

tool. Only identical variants retrieved from both assemblers were

selected.

Generating and filtering consolidated variant set of eachstrain

For each strain, all variants from the SV tools and the de novoassemblies were consolidated (S3A Fig.) and positioned to

consistent locations to be comparable using the tool Dindel

v1.01 [59]. In the case of contradicting or overlapping variants,

identical variants (having the same coordinates and length after re-

positioning) predicted by a majority of tools were chosen and the

rest discarded, or all were discarded if there was no majority.

Despite sequencing errors or cross-mapping artifacts of the re-

sequencing approach, genomic regions covered by reads are

generally trusted. Chances of long-range variations in the inner

50% of a mapped read’s sequence (‘‘inner core region’’ of a read)

are assumed to be low, since gaps would deteriorate the alignment

capability towards the ends of the read.

Therefore, we filtered out variants from the consolidated variant

set spanning a genomic region already covered by at least one

inner core region of a mapped read of the corresponding strain

(S3A Fig.), assuming homozygosity throughout the genome. This

‘‘core read criterion’’ had to be fulfilled at each genomic position

spanned by the variant.

Using branched reference to validate variantsVariants passing the core read filter in all strains were classified

as common variants and were incorporated into the reference

sequence of the previous iteration, thus replacing the reference

allele. Segregating variants, which could not be detected in all

strains, were additionally built into the reference in separate

‘‘haplotype regions’’ (or ‘‘branches’’ of the reference sequence) to

eventually be able to assess whether reads preferentially mapped to

the reference or the alternative haplotype sequence (S3A Fig.).

Linked variant haplotypes of a strain (distance between consec-

utive variants #107 bp, the maximal possible span of a read on the

reference) as well as identical haplotype regions among strains

were merged into one branch sequence.

For each strain, all reads were re-mapped to this new reference

sequence yielding read counts at the variant site on each branch

(rb) and at the corresponding site on the reference haplotype

sequence (rref) (S3A Fig.). Here, the read count of a site was

defined as the number of inner core regions spanning the site. To

increase certainty of variant calling and to rule out heterozygosity,

the read count of the major allele was tested against a binomial

distribution that assumed 95% allele frequency out of a total of rb+rref observations, i.e. sole presence of either the branch or the

reference haplotype (if 100% had been assumed, it would not yield

a distribution). The null hypothesis of homozygosity was rejected

after P value correction by Storey’s method [60] for q values below

0.05.

The same variant could be part of several different haplotypes

and thus, could be included into different branch sequences.

Reads supporting this variant would map at multiple locations in

the reference. Therefore, we allowed all aligned rather than only

unique reads to contribute to read counts and omitted the paired-

end correction procedure.

Final sets of common and segregating variantsWe followed a similar ‘‘population-aware’’ approach to prefer

commonalities among strains as was used for the SNP calling for

labeling variants as being common or segregating. Here, variable

sites with accumulated coverage over both branch and reference

sequence of #3x were marked as ‘‘missing data’’. If there was at

least one haplotype in a strain with a q value above 0.05, it was

assumed to be present in the population. If the test on the same

haplotype failed in another strain, but the absolute read count of

the haplotype sequence exceeded the alternative haplotype read

count by $2-fold, then this haplotype was considered present in

the corresponding strain as well.

We classified variants where at least 7 out of 13 strains did not

show missing data as ‘common’ if the branched haplotype was

present in all strains, as ‘not present’ if the reference haplotype was

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present in all strains, or into ‘segregating’ if there was support for

both haplotypes.

To combine common variants identified by the described

stepwise algorithm into potentially less evolutionary events, we

aligned 200 bp around each variant of the last iteration’s genome

back to the TAIR9 Col-0 reference genome using a global

alignment strategy (‘needle’ from the EMBOSS v6.3.1 package).

In total, we found 842,103 common and 2,017 segregating

polymorphisms without removing linked loci compared to Col-0

after two iterations, to which the different tools contributed to

different extent depending on the variant type (S3C Fig.).

Methylome sequencingGenomic and bisulfite sequencing were performed as described

in ref. [12].

Processing and alignment of bisulfite-treated readsThe procedure followed one described [12], except that we

aligned reads against the HPG1-like as well as against the Col-0

reference genome sequences. Command line arguments for

SHORE are listed in S1 File.

Determination of methylated sitesWe performed whole methylome bisulfite sequencing to an

average depth of 18x per strand (S5 Table) on two pools consisting

of 8-10 individuals per accession. We followed the same

procedures as described [12] to retrieve statistically significantly

methylated positions. Here, we restricted the set of analyzed

positions to cytosine sites with a minimum coverage of 3 reads and

sufficient quality score (Q25) in at least half of all strains (i.e. $7),

that is, 21 million positions in total. Out of those, we identified 3.8

million methylated cytosines in at least one strain by applying a

false discovery rate (FDR) threshold at 5%, and between

2,120,310 and 2,927,447 methylated sites per strain (S5 Table).

False methylation rates retrieved from read mapping against the

chloroplast sequence can be found in S5 Table. Using the HPG1

pseudo reference genome instead of the Col-0 reference genome

increased the number of cytosines sufficiently covered for statistical

analysis by 5% on average, and the number of positions called as

methylated by 7% (S5 Table).

Identification of differentially methylated positions(DMPs)

We performed the same methods as in ref [12] to obtain DMPs.

First, cytosine positions were tested for statistical difference

between both replicates of a sample using Fisher’s exact test and

a 5% FDR threshold. Because individual samples consisted of a

pool of several plants, the number of DMPs between replicates was

negligible (between 0 and 161). After excluding them, we applied

Fisher’s exact test on the 3.8 million cytosine sites methylated in at

least one strain for all pairwise strain comparisons. Using the same

P value correction scheme as in Becker et al., we identified

535,483 DMPs across all 13 strains.

DMPs identified in dependence of the number ofaccessions

Using the model developed in ref [61], a beta prior distribution

was estimated that determined the non-ancestral frequency for

each variable site. We assumed the methylation state in Col-0 to

be ancestral, which resulted in beta distribution parameters of

a = 0.029 and b = 0.644, corresponding to a mean non-ancestral

DMP frequency of 0.043 and a corresponding standard deviation

of 0.157. These were then used to estimate the fraction of common

DMPs that were expected to be found by sequencing a given

number of methylomes. Based on the formula presented in

supporting section 3 of ref [61], we estimated the total number of

DMPs in the population:

D(1)

N~

1

B(a,b)�

ð1

0

ha{1(1{h)Nind zb{1dh{

ð1

0

ha{1(1{h)2�Nind zb{1dh

� �

For Nind = 13 (the number of accessions in this study) and

D(1) = 1,046,892 (the total number of DMPs versus the Col-0

reference), we estimated a total number of possible DMPs in

the population of N = 59,770,415, which is close to the 43

million cytosines in the A. thaliana genome. Given such an

estimate for N, the D function can be evaluated numerically to

estimate the number of DMPs we would have detected had

we analysed the same number of accessions as in ref [10] (S8

Fig.).

Identification of methylated regions (MRs)The value of an approach that defines methylated regions

(MRs) before identifying differentially methylated regions

(DMRs) has been demonstrated before with a Hidden Markov

Model (HMM) method developed for the analysis of methyl-

ated-DNA-immunoprecipitation followed by array hybridiza-

tion (MeDIP-chip) [40]. An HMM based on next-generation

sequencing data was also applied to segment the maize

genome, which is much more highly methylated than the A.thaliana genome, into hypo- and hypermethylated regions

[62]. We modified the HMM implementation from Molaro

and colleagues [41] based solely on within-genome variation in

methylation rate. It assumes that the number of methylation-

supporting reads at each cytosine follows a beta binomial

distribution and that distributions over positions within and

between methylated regions will differ from each other,

providing a way to distinguish them. Thus, the model learns

methylation rate distributions for both an unmethylated and a

methylated state for each sequence context separately (CG,

CHG and CHH) while simultaneously estimating transition

probabilities between the two states from genome-wide data.

On the trained model, the most probable path of the HMM

along the genome is then used to define regions of high and

low methylation. The method of Molaro and colleagues was

designed for calling MRs in human samples, where the vast

majority of methylated cytosines are in a CG context. In

plants, however, one observes considerable methylation in all

three contexts (CG, CHG and CHH), each with a different

methylation rate distribution. Hence, we extended the HMM

by learning the parameters of three different beta binomial

distributions per state, one for each context. Additionally, in

contrast to humans, only the minority of cytosines in the CG

context is methylated, as are cytosines in the other contexts.

Hence, methylation rates were inverted to find hypermethy-

lated, rather than hypomethylated regions as in the original

HMM implementation.

Apart from these changes and some final filtering steps (see

below), we followed the same computational steps as described by

Molaro and colleagues [41]: The describing parameters of the – in

our case – six distributions (determining the emission probabilities)

and the transition probabilities between states were iteratively

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trained (using the Baum-Welch algorithm) from methylation rates

of all cytosines in the corresponding context throughout the

genome. After each iteration, all cytosines were probabilistically

classified into the most likely state via Posterior Decoding, given

the trained model. After training of the HMM, i.e. after maximally

30 iterations or when convergence criteria were met, consecutive

stretches of high methylation state were scored, in our case by the

sum of all contained methylation rates. Next, P values were

computed by testing the scores against an empirical distribution of

scores obtained by random permutation of all cytosines through-

out the genome. After FDR calculation, consecutive stretches in

high state with an FDR ,0.05 are defined as methylated regions

(MRs).

The HMM was run on all genome-wide cytosines, indepen-

dent of their coverage. Methylation rates were obtained using

accumulated read counts from the strain replicates, resulting in

one segmentation of the genome per strain. Gaps of at least 50

bp without a covered C position within a high methylation

state automatically led to the end of the high methylation

segment. Positions with a methylation rate below 10% at the

start or end of highly methylated regions (until the first position

with a rate larger than 10%), were assigned to the preceding or

subsequent low methylation region, respectively.

Identification of differentially methylated regions (DMRs)The method to identify MRs yielded 13 different segmen-

tations of the genome, one for each strain. We selected regions

being in different or highly methylated states between strains

and statistically tested them for differential methylation

(including FDR calculation). To obtain epiallele frequencies,

we clustered strains into groups based on their pairwise

comparisons and statistically tested the groupings against each

other. Regions that showed statistically significant methylation

differences between at least two sets of strains were identified

as DMRs. Finally, because of the sensitivity of the statistical

test, we empirically filtered DMRs for strong signals and

defined highly differentially methylated regions (hDMRs). All

these steps are described in depth in the following.

Selecting regions to test for differential methylationWe defined a breakpoint set containing the start and end

coordinates of all predicted methylated regions. Each combi-

nation of coordinates in this set defined a segment to perform

the test for differential methylation in all pairwise comparisons

of the strains, if at least one strain was in a high methylation

state throughout this whole segment (S12A Fig.). To also detect

quantitative differences rather than solely presence/absence

methylation, we also compared entirely methylated regions in

more than one strain to each other.

Because of the sheer number of such regions, we applied the

following greedy filter criteria: Regions were discarded from

any pairwise comparison if less than 2 strains contained at least

10 cytosines covered by at least 3 reads each (accumulated over

strain replicates) in this region (S12A Fig. (a)). Regions were

discarded from any pairwise comparison if the reciprocal

overlap of this region to at least one previously tested region

was more than or equal to 70% (S12A Fig. (b)). This was done

to prevent ‘‘similar’’ regions to be tested twice. Pairwise tests of

a region were not performed if both strains were in low

methylation state throughout the whole region (S12A Fig. (c)).

Strains were excluded from pairwise comparisons in a region if

the number of positions covered by at least 3 reads each was

less than half of the maximum number of such positions of all

strains in the same region (S12A Fig. (d)). This prevented

comparing regions with unbalanced coverage to each other,

e.g. a strain with 10 data points against another one with only

2.

These filters reduced the set of regions to test from ,2.5 million

to ,230,000 per pairwise comparison.

Testing regions for differential methylation betweenstrains

We designed a statistical test for differential methylation

between two strains for a given region. The test assumes that

the number of methylated and unmethylated read counts per

position along a region follows a beta binomial distribution –

similar to the HMM in MR calling. More precisely, there are 3

distributions for each sequence context and for each strain.

Using gradient-based numerical maximum likelihood optimi-

zation, we fitted the parameters for each beta binomial

distribution on the available read count data of the region in

the respective strain. This was done a) for each of the two

strains separately (while taking strain replicates into account),

resulting in (two times three) strain-specific beta binomial

distributions, and b) for the read counts of both strains

including their replicates together, resulting in (three) commonbeta binomial distributions. In this way, we obtained each

distribution’s mean m and standard deviation s. We selected

only regions for potential DMRs, whose intervals [m1 – 2s1, m1

+ 2s1] for strain 1 and [m2 – 2s2, m2 + 2s2] for strain 2 did not

overlap.

To further corroborate statistical significance, we computed

P values by calculating the ratio of the strain-specific and the

common log likelihoods of the available read count data using

the corresponding beta binomial distributions and by testing it

against a chi-squared distribution (with 6 degrees of freedom).

Let sample S have NSc cytosines in context c in total and CScp

reads at position p in context c, from which xScp are

methylated, then we compute:

After correction for multiple testing using Storey’s method [60],

an FDR threshold of 0.01 defined statistically different methylated

regions (DMRs) between two strains.

Additionally, this method allowed calling differential methyla-

tion in a region for each context separately by computing P values

as described above without summing over the contexts (c = 1, 2 or

3). We termed resulting DMRs CG-DMRs if the methylation at

X3

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only CG sites within this region was statistically significantly

different, and similarly CHG-DMRs and CHH-DMRs.

Grouping differentially methylated strains in each regionFor 13 strains there are at maximum 78 pairwise comparisons

per region. To summarize pairwise comparisons and obtain

epiallele frequencies, we assigned strains into differentially

methylated groups. To achieve such clustering, we constructed a

graph for each region where strains were represented as vertices

and connected to other strains by an edge if the region was

identified as a DMR between them (S12B Fig.). We assume that

strains within a group are then similarly methylated. The task is to

find the smallest number of groups of vertices so that no two

strains within a group are connected by an edge.

We set up a custom algorithm, which iteratively solves the

‘‘vertex coloring problem’’ for an increasing number of different

colors, starting with two and quitting once all strains could be

successfully assigned a color (S12B Fig.). In each iteration, strains

were processed in descendent order of their degree (i.e. number of

edges it is connected to). Each strain was assigned to all possible

colors that did not invoke a collision. Subsequently, the algorithm

continued recursively to assign the color of the next strain.

Each strain had 3 context-dependent means of its beta binomial

distributions per region (termed strain means from now on). We

roughly approximated each group’s mean methylation values

(group means) as the mean values of all strain means within a

group. The grouping diversity describes the accumulated absolute

differences between the strain means and their respective groupmeans divided by the number of strains. As an example, consider

S12B Fig. For simplicity, it only displays methylation rates for one

out of three contexts. In the real data, the respective values were

accumulated over all three contexts. The group mean for the blue

strains in the example is (89+90+90+93+87)/5 = 89.8% and for

the white strains 52%. The grouping diversity of the clustering

shown here would be (from strains A to K): (|56–52|+|59–52|+|64–52|+|89–89.8|+|41–52|+|93–89.8|+|90–89.8|+|45–52|+|47–52|+|90–89.8|+|45–52|+|87–89.8|)/11 = 2.84.

If there was more than one possible grouping of the strains, we

chose the one with the lowest grouping diversity. A strain with no

edges (i.e. which is not statistically differentially methylated to any

other strain) was assigned into the group to which the accumulated

absolute difference between its strain mean and the group meanwas lowest. In the example of S12B Fig., strain L is grouped to the

blue strains because its mean methylation value (81%) is closer to

the blue group mean (90%) than to the white one (52%).

This procedure summarized the ,221,000 DMRs of all

pairwise strain comparisons into 11,323 DMRs between groups

of strains.

Testing regions for differential methylation betweengroups of strains

Once grouped, the same statistical test as for differential

methylation between two strains was used to test groups of strains.

Beta binomial distributions were approximated using the read

counts of all strains in a group as if they were replicate data. This

procedure identified 10,645 groups of regions showing significantly

different methylation. Because the method used for the selection of

the regions to perform the differential test can result in overlapping

regions, DMRs can still overlap each other. From sets of

overlapping DMRs, the non-overlapping DMR(s) with the lowest

‘grouping diversity’ was (were) retained, resulting in 4,821 final

DMRs. For the vast majority of DMRs (98%), strains were

classified into two groups, i.e. there are only two epialleles.

Identification of highly differentially methylated regions(hDMRs)

Our sensitive statistical test classified as differential some regions

with low variance and only subtle methylation difference; we

therefore defined as highly differentially methylated regions

(hDMRs) with potentially greater biological relevance all DMRs

that were longer than 50 bp and that showed a more-than-three-

fold difference in methylation rate in at least one sequence context,

when considering at least five cytosines of that context (S12 Fig.).

In addition, the overall methylation rate of the DMR in the more

highly methylated strain had to be greater than 20%. Of 3,909

size-filtered DMRs, 3,199 (80%) were classified as hDMRs (S8

Table). The grouping of hDMRs yielded similar epiallele

frequencies as for the DMPs (54% with frequency larger than 1;

Fig. 2F).

Epigenetic variation in HPG1 lines and methylation-deficient mutants

The data from Stroud and colleagues [43] contain position-wise

methylation rates for each sample. We defined a single site as

methylated in wild type (WT) if both Col-0 samples Col_-

WA034L3 and Col_WB023L8 had a methylation rate of 10% or

higher, and if at least one of them is more than 20% methylated.

We declared a site in a mutant sample as having ‘lost’ methylation

where the wild type was methylated and the mutant showed a

methylation rate of less than 10%. In contrast, a ‘gained’

methylation site had less than 10% methylation in at least one

of the WT samples and more than 20% methylation in the

mutant. To assess if epigenetic variation in the HPG1 lines is

enriched at sites affected by impaired methylation machinery, for

each mutant, we constructed a set of positions, which were

methylated in WT, covered in the mutant sample (i.e. present with

a rate in the mutant sample file), and which were covered in the

HPG1 and MA populations. A site was considered covered in a

population when more than half of the strains showed a high

quality and a more than 3-fold covered base call (see ‘Determi-

nation of methylated sites’ or [12]). For those positions and

different subsets thereof, the fractions of sites with gained or lost

methylation in the mutant compared to the wild type samples were

plotted in S19 Fig.

Heritability analysis of methylated regionsFor each differentially methylated region, we considered a linear

mixed model to estimate the proportion of variance that is

attributable to genetic effects (heritability) and its standard error.

The approach is similar to variance component models used in

GWAS, e.g. refs. [63,64]. Briefly, we considered the log average

methylation rate of DMRs as phenotype and assessed the variance

explained by genotype using a Kinship model constructed from all

segregating genetic variants. We considered only DMRs and

genetic polymorphisms that had no missing data in all accessions.

Population structure analysisWe identified non-synonymous SNPs using SHOREmap_anno-

tate [65] and excluded them from population structure analyses. We

ran STRUCTURE v.2.3.4 [66] with K = 2 to K = 9 with a burn-in

of 50,000 and 200,000 chains for 10 repetitions and determined the

best K value using the DK method [67]. The phylogenetic network

was generated using SplitsTree v.4.12.3 [68].

Mapping to genomic elementsWe used the TAIR10 annotation for genes, exons, introns and

untranslated regions; transposon annotation was done according

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to [69]. Positions and regions were hierarchically assigned to

annotated elements in the order CDS. intron. 59 UTR. 39

UTR. transposon. intergenic space. We defined as intergenic

positions and regions those that were not annotated as either CDS,

intron, UTR or transposon.

Positions were associated to the corresponding element when

they were contained within the boundaries of that element.

(D)MRs were associated to a class of element if they overlapped

with that class of element; a (D)MR could only be associated to

one class of element. When summing up basepairs of an element

class covered by (D)MRs, the number of basepairs of a (D)MR

overlapping with that class of element were considered. In that

case the space covered by a (D)MR could be assigned to different

classes of elements, while each basepair of the (D)MR could be

assigned to only one class.

Overlapping region analysisWe tested for significant overlap of DMRs using multovl version

1.2 (Campus Science Support Facilities GmbH (CSF), Vienna,

Austria). We reduced the genome space to the basepair space

covered by MRs identified in at least one HPG1 accession. DMRs

were considered in the analysis if their start and end positions were

contained within the MR space. DMRs that only partially

overlapped with the MR space were trimmed to the overlapping

part. Overlap between DMRs from different datasets was analyzed

by running 100,000 permutations of both DMR sets within the

MR basepair space. multovl commands are listed in S1 File.

Processing and alignment of RNA-seq readsReads were processed in the same way as genomic reads, except that

trimming was performed from both read ends. Filtered reads were then

mapped to the TAIR9 version of the Arabidopsis thaliana (http://

www.arabidopsis.org) genome using Tophat version 2.0.8 with Bowtie

version 2.1.0 [70,71]. Coverage search and microexon search were

activated. The command lines for Tophat are listed in S1 File.

Gene expression analysisFor quantification of gene expression we used Cufflinks version

2.0.2[72]. We ran a Reference Annotation Based Transcript

assembly (RABT) using the TAIR10 gene annotation (ftp://

ftp.arabidopsis.org/home/tair/Genes/TAIR10_genome_release/

TAIR10_gff3/) supplied with the most recent transposable element

annotation [69] Fragment bias correction, multi-read correction

and upper quartile normalization were enabled; transcripts of each

sample were merged using Cuffmerge version 2.0.2, with RABT

enabled. For detection of differential gene expression we ran

Cuffdiff version 2.0.2 on the merged transcripts; FDR was set to ,

0.05 and the minimum number of alignments per transcripts was

10. Fragment bias correction, multi-read correction and upper

quartile normalization were enabled. The command lines for the

Cufflinks pipeline are listed in S1 File. Analysis and graphical

display of differential gene expression data was done using the

cummeRbund package version 2.0.0 under R version 3.0.1.

Data visualizationWhen not mentioned otherwise in the corresponding para-

graph, graphical displays were generated using R version 3.0.1

(www.r-project.org). Circular display of genomic information in

Fig. 2A was rendered using Circos version 0.63 [73].

PhenotypingLeaf area was determined using the automated IPK LemnaTec

System and the IAP analysis pipeline [74]. Plants were grown in a

controlled-environment growth-chamber in an alpha-lattice design

with eight replicates and three blocks per replicate, taking into

account the structural constraints of the LemnaTec system. Each

block consisted of eight carriers, each carrying six plants of one

line. Stratification for 2 days at 6uC was followed by cultivation at

20/18uC, 60/75% relative humidity in a 16/8 h day/night cycle.

Plants were watered and imaged daily until 21 days after sowing

(DAS). Adjusted means were calculated using REML in Genstat

14th Edition, with genotype and time of germination as fixed

effects, and replicate|block as random effects.

Environmental variabilityLocal temperature and liquid precipitation data was calculated

from National Climatic Data Center (NCDC) Global Summary of

Day (GSOD) data. Collection locations were matched to the

closest weather station with ,5% missing data for five years prior

to the collection date. Cumulative liquid precipitation was

calculated each year starting from January 1.

Data accessibilityThe DNA and RNA sequencing data have been deposited at

the European Nucleotide Archive under accession number

PRJEB5287 and PRJEB5331. A GBrowse instance for DNA

methylation and transcriptome data is available at http://gbrowse.

weigelworld.org/fgb2/gbrowse/ath_methyl_haplotype1/. DNA

methylation data, MR coordinates and genetic variant informa-

tion have also been uploaded to the genome browser of the EPIC

consortium (https://www.plant-epigenome.org/; https://

genomevolution.org/wiki/index.php/EPIC-CoGe) and can be

accessed at http://genomevolution.org/r/939v. The software of

our methylation pipeline can be downloaded at http://

sourceforge.net/projects/methpipeline.

Supporting Information

S1 Fig Recent temperature histories of samples. Samples were

matched to the closest weather stations (distance in km). Daily

temperature range (dark bars) and means (white points) of 330

days prior to the collection date, such that late year data reflect

temperature ranges of the previous year. Five-year temperature

ranges (light bars) and means (white line) indicate longer-term

temperature variability. Different collection dates and locations

both contribute to different means and variance of recent

temperatures experienced by samples.

(EPS)

S2 Fig Recent precipitation histories of samples. Cumulative

(from January 1) liquid precipitation at the weather stations closest

to each collection site (distance in km). Black lines show yearly

histories for five years prior to collection, the thick black line

indicates the cumulative history of the previous 330 days. Blue line

shows the LOESS estimate of mean precipitation accumulation

over a year.

(EPS)

S3 Fig Iterative re-alignment strategy and statistics. (A) Iterative

re-alignment approach to evaluate predicted variants and to build

a HPG1 pseudo-reference genome. (1) For each strain, variants

called by diverse structural variant detection tools and a local denovo assembly pipeline were combined into a consolidated variant

set. (2) Variants with core read coverage were filtered out, the

remaining variants were classified into common and potentially

segregating. Brown triangles symbolize insertions/deletions, the

brown X represents a SNP. (3) All common variants were

incorporated into the reference genome. All segregating variants

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were introduced in branches of the reference genome, which

incorporated polymorphisms linked by less than 107 bp. (4) After

mapping the reads against the branched reference, a binomial test

was performed in each strain and for each variable site to call the

allele, i.e., to determine whether there was statistical evidence for

the presence of only one haplotype covering the variant’s

coordinates. Variants with the same non-reference allele call in

all strains were considered as ‘‘common’’; those with a reference

call in at least one strain and a variant call in at least one other

strain were classified as ‘‘segregating’’. (5) All common variants

from the previous step were incorporated into the new reference

sequence, and a new iteration was started over from (1), or this

new genome served as the HPG1 pseudo-reference genome after

iteration 2. (B) Increase of detected variants and decrease of

unsequenced genome space and unmappable reads by iterative

read mapping. The legend on the right side denotes absolute

values after iteration 2. The reference value (100%) derives from

the mapping against the Columbia-0 genome sequence (TAIR9),

and for common variants it is the number of variants leading to the

genome of iteration 1. Thus, ,842,000 common variants led to

the genome of iteration 2, ,864,000 to the genome of iteration 3.

(C) Composition of common polymorphisms by variant type (top)

and by detection tool (bottom). Variants found by more than one

tool contributed to the count for all respective tools.

(EPS)

S4 Fig Distribution of genetic variants along the five chromo-

somes. Relative density of common variants in 100 kb sliding

windows with a step size of 10 kb. SNP: single nucleotide

polymorphism, SV: structural variant.

(EPS)

S5 Fig Annotation of genetic variants. Polymorphisms were

hierarchically assigned to CDS. intron. 59 UTR. 39 UTR.

transposon. intergenic. HPG1: haplogroup-1 lines, SNP: single

nucleotide polymorphism, SV: structural variant.

(EPS)

S6 Fig Magnification of the central area of the phylogenetic

network in Fig. 1c. Numbers indicate bootstrap confidence values

(10,000 iterations).

(EPS)

S7 Fig Phenotypic analysis. (A) Leaf growth measured over time

(Materials and Methods). Error bars represent 95% confidence

intervals. On average, 36 plants were measured per accession. (B)

Correlation of genetic distance, represented by number of SNPs

per pairwise comparison, and difference in leaf area at 21 days

after germination.

(EPS)

S8 Fig Number of DMPs in dependence of the number of

sequenced accessions. (A) Fraction of all DMPs of different allele

frequencies that can be detected by sequencing a given number of

accessions. We assumed the methylation state in the Col-0

reference as the ancestral state. Vertical lines indicate the number

of accessions in this study (13) and in ref [10] (140). f: relative

derived allele frequency. (B). Estimate of the number of DMPs

detected by sequencing a given number of HPG1 accessions.

Vertical lines mark the number of accessions sequenced in this

study and in ref [10].

(EPS)

S9 Fig Differentially methylated positions (DMPs). (A) Epiallele

frequencies of DMPs for CG sites only (left), and comparison of all

three sequence contexts (right). (B) Annotation of invariantly (N-

DMPs) and differentially (DMPs) methylated sites. Cytosines were

hierarchically assigned to CDS. intron. 59 UTR. 39 UTR.

transposon. intergenic.

(EPS)

S10 Fig Effect of sample pooling on the number of identified

DMPs. Small filled triangles and box-and-whisker plot indicate

distribution of differentially methylated positions (DMPs) that were

called by comparing individual plants of generations 31 and 32 of

mutation accumulation (MA) lines 30–39 and 30–49 with

individual plants of 0-4-26 and 0-8-87, which represent generation

3 of two independent lines (16 comparisons in each group). The

large unfilled triangles indicate the number of DMPs that were

called when data from four lines of generations 31 and 32 were

pooled in silico and compared against all four lines from

generation 3. On average, a substantially lower number of DMPs

is called with pooled data.

(EPS)

S11 Fig Characteristics of (differentially) methylated regions. (A)

Histogram of the length of unified methylated regions (MRs),

differentially and highly differentially methylated regions (DMRs

and hDMRs). The red and green lines indicate mean and median

length, respectively. For better visibility, regions larger than 2,000

bp were excluded from the representation. The minimum size of

hDMRs was 50 bp. (B)-(D) are based on data of one HPG1 strain

(LISET-036). (B) Number of unmethylated cytosines (Cs) in-

between methylated CG sites (mCGs) within genes in dependence

of whether these sequences are inside or outside of MRs. (C)

Distances in bp between methylated CG sites within genes in

dependence of whether these sequences are inside and outside

MRs (minimal distance 2 bp). (D) Distances between methylated

CG sites within body methylated (BM) genes identified with the

method from ref. [48] (SOM: Validation of methylated regions)

and within genes not identified as BM (minimal distance 2 bp). (E)

Length distributions of DMRs that overlap and that do not

overlap coding regions. Triangles show mean values.

(EPS)

S12 Fig Selecting parts of methylated regions to test for

differential methylation. (A) Example illustrating the selection

procedure of regions and pairwise strain comparisons to be tested

for differential methylation. (*) For simplicity, the illustration uses a

minimum number of covered sites of two reads per region (10

reads for the real data set). (1) We selected all possible regions

where two strains presented different states of methylation (reg1 to

reg5) and applied filter criteria (a), (b) and (c). (2) If a region passed

filters (a), (b) and (c) (in the example only reg1 and reg2), criteria

(a), (c) and (d) were checked for each pairwise comparison between

strains on that region. Note, the selection of a region in (1) must

not necessarily lead to a differential test between any two strains

(e.g., reg1). Refer to the Material and Methods section for

elaborate descriptions of criteria. (B) Assignment of strains to

different groups based on differential methylation. Left: An

exemplary differentially methylated region (DMR) represented as

a graph: strains are represented as nodes, edges reflect a

statistically significant test between two strains. Right: Finding

the minimal number of sets, where no edge connects nodes from

two different groups is known as the colouring vertex problem. In

this example, the solution is two sets of strains (blue and white

nodes). Strains without statistically significant tests (e.g., strain L)

are grouped into the set of strains where the difference between the

strain’s and the group’s mean methylation rate is minimal.

(EPS)

S13 Fig Methylation rate differences in regions of differential

methylation. Histograms of the absolute mean methylation rate

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differences of differentially methylated regions (DMRs; grey) and

highly differentially methylated regions (hDMRs; black) of all

different sequence contexts.

(EPS)

S14 Fig Differential gene expression. (A) Hierarchical clustering

of HPG1 accessions by expression of differentially expressed genes.

(B) Differentially expressed genes per pairwise comparison.

FPKM: fragments per kilobase per million mapped reads.

(EPS)

S15 Fig Differentially methylated regions (DMRs) and gene

expression. Examples of DMRs (top panel) overlapping with a

protein-coding gene (AT4G09360, left), a non-coding RNA

(AT4G04223, middle) and a transposable element (AT1G62460,

right). The expression of the corresponding locus is represented in

the bottom panel. FPKM: fragments per kilobase per million

mapped reads, 5mCG/5mCHG/5mCHH: methylated cytosine of

respective context.

(TIF)

S16 Fig Distribution and annotation of single site epimutations

in two populations. (A) Relative density of differentially methylated

positions (DMPs) along the 5 Arabidopsis chromosomes according

to their status of overlap between mutation accumulation (MA)

and haplogroup-1 (HPG1) lineages. For each class and each

chromosome, the window with the maximal density was set to 1.

Sliding window; window size 100,000 bp; step size 10,000 bp. (B)

Ratios between epimutation frequencies and sequencing depth

along the 5 chromosomes for MA and HPG1 lines. Epimutation

frequencies were determined as the number of DMPs per cytosine

with at least threefold coverage per window. Coverage is

represented as average coverage per window across all accessions

of each population. Dashed lines mark the balanced coverage ratio

of 1. Sliding window; window size 100,000; step size 10,000 bp. (C)

Annotation of all cytosines (Cs), non-differentially methylated

positions (N-DMPs) and DMPs according to their overlap between

MA and HPG1 lineages. Cytosines were hierarchically assigned to

CDS. intron. 59 UTR. 39 UTR. transposon. intergenic.

(EPS)

S17 Fig Overlap of MA and HPG1 DMPs according to MA

generational distance. We computed differentially methylated

positions (DMPs) between two randomly chosen mutation

accumulation (MA) strains separated by specific numbers of

generations and plotted the fraction of those DMPs shared with a

randomly chosen haplogroup-1 (HPG1) strain. Each boxplot

summarizes ten such random comparisons.

(EPS)

S18 Fig Overlap of MA and HPG1 DMRs per genomic feature

and DMR sequence context. Differentially methylated regions

(DMRs) were hierarchically assigned to CDS. intron. 59 UTR.

39 UTR. transposon. intergenic. CG-DMRs are differently

methylated regions in the CG context only and C-DMRs in any

other (additional) context(s).

(EPS)

S19 Fig Epigenetic variation in HPG1 lines and methylation-

deficient mutants. (A, B) Fraction of sites that lost (A) or gained (B)

methylation in mutant samples compared to two wild type (WT)

Col-0 samples [43], from all methylated positions in WT, for each

subset of these sites according to the status in the haplogroup-1

(HPG1) and mutation accumulation (MA) population (DMP:

differentially methylated position). The set of all methylated

positions in the wild type samples was restricted to sites covered in

both HPG1 and MA lines as well as in the mutant sample (see

Methods). (C, D) Fraction of sites that lost (C) or gained (D)

methylation in mutant samples compared to WT samples, from all

methylated positions in WT. Plotted are the fractions from all

covered sites in all samples and from sites covered by DMRs

within the haplogroup-1 lines that overlap regions of the genome

with methylation occurring only in the CG context (mCG) or in

any additional or alternative context(s) (mC).

(EPS)

S20 Fig Heritability of hDMRs by sequence context and status

in MA lines. Distributions of heritability values of highly

differentially methylated regions (hDMRs) according to significant

sequence context and methylation status of overlapping regions in

the mutation accumulation (MA) lines.

(EPS)

S21 Fig Hierarchical clustering by differentially and invariantly

methylated positions and regions. DMPs: differentially methylated

positions, DMRs: differentially methylated regions, N-DMPs: not

differentially methylated positions, N-DMRs: not differentially

methylated regions.

(EPS)

S22 Fig Analysis of hDMRs unique to LI-SET-036. (A) The

number of strains sharing the same methylation status for highly

differentially methylated regions (hDMRs) found in each strain is

plotted (determined by the strain grouping procedure; see

Methods). (B) Stacked bar plots showing the distributions of

sequence contexts (bottom) and overlapping genomic features (top

three plots) for hDMRs unique to each strain. ‘CG only’

exclusively considers CG-hDMRs whereas ‘CHG’ and ‘CHH’

might additionally include hDMRs of other contexts than CHG

and CHH, respectively. The distribution across intergenic space,

TEs and genes was similar for all strains. See section ‘‘Analysis of

LI-SET-036 specific hDMRs’’ above for more details.

(EPS)

S1 Table Haplogroup-1 (HPG1) accessions used in this study.

(ODS)

S2 Table Summary statistics on genome sequencing.

(ODS)

S3 Table Common SNPs and SVs.

(TXT)

S4 Table Segregating SNPs and SVs.

(TXT)

S5 Table Summary statistics on methylome sequencing.

(ODS)

S6 Table Methylated regions (MRs).

(TXT)

S7 Table Differentially methylated regions (DMRs).

(TXT)

S8 Table Highly differentially methylated regions (hDMRs).

(TXT)

S9 Table Summary statistics on transcriptome sequencing.

(ODS)

S10 Table Differentially expressed (DE) genes identified in

pairwise comparisons between HPG1 accessions. All DE genes (q-

value ,0.05) between any two accessions are listed. If more than

one gene name appears in column 1, the read counts could not be

assigned to one gene in particular and/or a fused transcript was

suggested by the read data.

(ODS)

Stability of the Arabidopsis thaliana Methylome in Nature

PLOS Genetics | www.plosgenetics.org 16 January 2015 | Volume 11 | Issue 1 | e1004920

S11 Table Statistics of overlapping DE genes and hDMRs.

(ODS)

S12 Table Overlap of SVs with MRs in HPG1 and Col-0 in

different genomic features.

(ODS)

S13 Table Scoring matrices for SNP calling and assessing

cytosine site statistics (for bisulfite sequencing).

(ODS)

S1 File Command lines for software packages used in this study.

(PDF)

Acknowledgments

We are grateful to C. Lanz for help with Illumina sequencing, Q. Song and

A. Smith for making the source code of their Hidden Markov Model

implementation available, the group of V. Colot for sharing Col-0 MeDIP-

Seq data, and C. Klukas for assistance with the processing of the

phenotyping data. We thank R. Schwab for critical reading of the

manuscript.

Author Contributions

Conceived and designed the experiments: CB JH TA JB DW. Performed

the experiments: CB RCM. Analyzed the data: JH CB JM OS GW KS.

Contributed reagents/materials/analysis tools: JB JF. Wrote the paper: CB

JH DW. Provided advice on statistical analysis: KB.

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